WO2011141586A1 - Procédé pour calculer la perception d'expérience d'utilisateur de la qualité des services surveillés intégrés chez des opérateurs de télécommunications - Google Patents

Procédé pour calculer la perception d'expérience d'utilisateur de la qualité des services surveillés intégrés chez des opérateurs de télécommunications Download PDF

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Publication number
WO2011141586A1
WO2011141586A1 PCT/ES2010/070324 ES2010070324W WO2011141586A1 WO 2011141586 A1 WO2011141586 A1 WO 2011141586A1 ES 2010070324 W ES2010070324 W ES 2010070324W WO 2011141586 A1 WO2011141586 A1 WO 2011141586A1
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WIPO (PCT)
Prior art keywords
quality
experience
user
services
perception
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PCT/ES2010/070324
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English (en)
Spanish (es)
Inventor
Antonio CUADRA SÁNCHEZ
María del Mar CUTANDA RODRÍGUEZ
Antonio Liotta
Vlado Menkovski
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Telefonica, S.A.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Application filed by Telefonica, S.A. filed Critical Telefonica, S.A.
Priority to PCT/ES2010/070324 priority Critical patent/WO2011141586A1/fr
Priority to EP10851321.9A priority patent/EP2571195A4/fr
Priority to BR112012029162A priority patent/BR112012029162A2/pt
Priority to US13/697,891 priority patent/US20130148525A1/en
Priority to ARP110101647A priority patent/AR081041A1/es
Publication of WO2011141586A1 publication Critical patent/WO2011141586A1/fr

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/147—Network analysis or design for predicting network behaviour
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5061—Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the interaction between service providers and their network customers, e.g. customer relationship management
    • H04L41/5067—Customer-centric QoS measurements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
    • H04L65/80—Responding to QoS
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M3/00—Automatic or semi-automatic exchanges
    • H04M3/22—Arrangements for supervision, monitoring or testing
    • H04M3/2227—Quality of service monitoring

Definitions

  • the present invention refers to a method for calculating the perception of user experience of the quality of the monitored services integrated in telecommunications operators.
  • the main field of application is innovation in monitoring services in telecommunications operators.
  • the present invention comprises a method that proposes to use data from the monitoring of the services used by the users together with questionnaires previously filled out by a representative sample of users for later mixing by means of correlation algorithms and after being passed through algorithms of Automatic learning to obtain from them a value of the quality of the experience that supposes an estimate of the quality of the service perceived by the user who makes use of said service.
  • POTS Old Ordinary Telephone Service
  • MOS Mean Opinion Score
  • the average opinion score is a numerical figure that estimates the perceived quality of a conversation service, expressed within a whole range of 1 to 5, where 1 is the lowest perceived quality, and 5 is the Higher perceived quality.
  • MOS tests for voice are specified in ITU-T Recommendation P.800 "Methods for subjective determination of transmission quality".
  • PESQ Voice Quality Perception Assessment
  • PSQM Voice Quality Perception Measurement
  • PESQ voice quality perception
  • the original (reference) signal is compared with the received (degraded) signal and a PESQ score is calculated as a prediction of the subjective quality of each test pulse, which is performed using active probes.
  • PEVQ Advanced Video Quality Perception Assessment
  • ITU-T Recommendation P.563 defines a "Unique method of objective evaluation of high quality voice in narrowband telephony applications". However, the method is based on knowledge about human language, so it is not necessary to use real users as input. The results are not very accurate because they must be used together with PESQ.
  • Quality of user experience mainly includes the calculation of MOS from intrusive models (PESQ for VoIP, PEVQ for video) that They take into account user opinions only when it is defined by the model and is the only one involved. This may be valid for stable services such as VoIP in PESQ, but it is not valid for strongly dependent content services such as IPTV or MobileTV.
  • Opinion polls are not part of current models, so there is no temporary comparison with network or business indicators.
  • the models that calculate the MOS with user perception are based on intrusive tests, using the corresponding QoE measurement platform based on active probes. Some alternatives may use non-intrusively data sources, but they cannot be considered as QoE measurement platforms but service quality measurement (QoS) platforms.
  • QoS service quality measurement
  • the characteristics of each session are collected in a specific detailed record (XDR or IPDR for IP networks), which contains the essential data for quality purposes.
  • the data sources of these procedures are the protocol data units, usually obtained from the passive probes installed in the monitoring network.
  • XDR Generic Detailed Registration
  • a user service containing network, service and user data is reconstructed, in terms of the quality of the experience
  • Some models such as P.563 calculate the quality figures in a passive manner only for conversation services, without taking into account the validation of the users in the model, so they cannot be considered as real QoE monitoring solutions. This means that the procedures cannot be used to handle a large amount of data if it is used with the traffic of real users.
  • XDRs include the information of any user when using any service, but only from the network and service perspective since the data sources are only telecommunications systems, and does not involve the user in any way.
  • the present invention describes a method for calculating the customer's perception of user experience within a telecommunications operator, such as voice, video, multimedia data, etc., which is based on different data profiles (passive monitoring of real user data and surveys to optimize accuracy) and that includes the correlation of both data profiles to give a single final perspective of customer perception.
  • This method is supported by a network monitoring system.
  • the questionnaires of the levels of quality perceived by the client will be used to adjust the QoE, by establishing a series of limits and thresholds that are applied in the monitoring indicators in order to establish some benchmarks in terms of perception.
  • the input data of the input network consists of a set of indicators for each service used, they are collected by passive probes deployed throughout the monitoring network (for example, XDR). This data provides a real view of the service of any user, since all of them are permanently monitored.
  • These indicators include a wide variety of parameters of the multimedia coding domain, transport, as well as the terminal in which the media are presented and, finally, the type of content that the user is experiencing.
  • This QoE approach analyzes the correlation of all these parameters to maximize user experience and minimize provider resources.
  • the procedure generates a QoE value that can be named as an estimated experience score, for any user when using a service, which shows the satisfaction perceived when using the service by the end user.
  • This QoE value will also be included in the XDR, in order to be part of the monitoring information.
  • the present invention has the following advantages over known solutions:
  • This invention can predict how each customer receives the services they use, without asking about their experience.
  • the input information is extracted exclusively from the monitored network systems already deployed.
  • the methodology object of the invention allows an accurate prediction of the values of the QOS MOS starting from small subjective initial studies in real-time environments. This is done through an innovative approach to the use of automatic learning algorithms for the construction of prediction models in the data from subjective studies.
  • the solution can provide a realistic view of a service used by any user based on a single indicator (MOS), its accuracy and the attributes of the quality of service that have contributed to the perception that customers have of said service. service.
  • MOS single indicator
  • this method can be applied to a network operator or service provider to have a reliable tool to know what the customer opinion of any service is. Therefore, this method allows a realistic approach to QoE monitoring, which can be used for different purposes, such as service planning, marketing campaigns and more precise management of business relationships with customers.
  • the present invention consists on the one hand of a method for calculating the perception of user experience of the quality of the monitored services integrated in telecommunications operators, any type of service being able to be monitored.
  • This procedure includes at least as input data, network data obtained through monitoring platforms previously deployed in network operators of the services used by some users and experience questionnaires related to a service used that have been previously filled in by a set of users, characterized in that it comprises the following phases: i) mix, for each question of the experience questionnaire, the network data together with the answers to that question using conventional correlation algorithms;
  • iv) combine the prediction models generated in the previous phase through a weighted voting system generating a single final prediction model; and, v) generate an MOS experience quality value for each network data through a platform for predicting the quality of experience in which the prediction model generated in phase iv) is integrated.
  • the correlation algorithms of the phase of mixing the network data, phase i) identify the network data and the data of the questionnaires that are mixed by means of a unique identification key of the user identifier fields comprising a number telephone number of the user who has filled in the questionnaire and an IP address assigned to said user of the identifier of the content served where the type of content and time stamp of the service is specified, which includes the moment in which the service was used.
  • the training data that is stored in phase ii) contains the most significant parameters that they contribute to the quality of experience being said parameters selected, when it comes to services offered over IP networks, among, type of content, result of the service, user agent, sequence losses, assent losses, packet loss rate, percentage of packet loss, burst packet loss, maximum, minimum and average performance values, delay and delay variance and a combination thereof.
  • the aforementioned network data that are monitored to be used as input of the invention comprise information about services on IP networks offered by telecommunications operators selected from Television on IP (IPTV, TVoDSL, HDTVoIP, IPMS based on IMS, TV on FTTH, TV on GPON, TV on WiMax, mobile TV, 3G TV, 4G TV, videostreaming, Internet TV, IPTV-DTH) and its subservices (video on demand, pay per view, multicast TV, general broadcast TV, broadband multicast hybrid (HbbTV), P2PTV), Telephony over IP (VoIP, VoIP, telephone over Internet, voice over broadband (VoBB), VoIP based on IMS, ToIP, videotelephony over IP, conference call over IP) and its subservices (voice, data , instant messaging, presence, registration), Internet services (web browsing, email, file hosting, videostreaming, XML transactions) and particular services of telecommunications operators (Mensa jer ⁇ a, MMS, SMS, signaling, SS7, roaming,
  • the training record generated in phase iii) comprises the most significant parameters to contribute to the calculation of the user experience, said parameters being selected, when it comes of services offered over IP networks, among, type of content, result of the service, user agent, sequence losses, loss of assent, lost packet rate, percentage of packet loss, burst packet loss, maximum, minimum values and means of performance, delay and variance of the delay and a combination thereof.
  • the automatic learning algorithm of this phase automatically selects the parameters based on their relevance to the quality prediction.
  • the most significant parameters can be any of those available by the network monitoring system, although the most common are the performance, the lost packet rate and the delay.
  • the votes of the weights of phase v) are modeled using automatic learning regression models and the experience quality prediction platform comprises parameters selected from:
  • Network parameters that contribute to the calculation of the user experience selected among content type, service result, user agent, sequence losses, assent losses, packet loss rate, percentage of packet loss, burst of loss of packages, maximum, minimum and average values of the performance, delay and variance of the delay and a combination thereof; Y,
  • the machine learning algorithm of phase iii) of the method automatically identifies the network parameters that most affect QoE based on their relevance to quality prediction. This takes place in order to propose the necessary values to achieve a user-defined quality of experience. This procedure includes the following stages:
  • the QoE algorithm indicates a certain number of parameters and the values that it should take (increase or decrease) to improve the user experience. This automatic procedure will depend on the training model, the values that the particular parameters take for each session, and the quality of the expected experience. In fact, the algorithm is able to identify the parameters that have most sensitively contributed to the perception by proposing a threshold for each session from which the quality of the experience would be desirable.
  • the QoE prediction model that will be applied to the prediction platform is established.
  • the second (stationary) phase uses it in a stationary manner, taking as input the network data, and generating an MOS value for each data network.
  • the network data is obtained from the monitoring network, such as PSTN, PLMN, ATM, Frame Relay, SDH, PDH, TDM, SS7, GSM, GPRS, UMTS, HSDPA, HSUPA, LTE, SAE, WiMAX, Wi- Fi, IP, MPLS, NGN, IMS, IPTV, MobileTV, etc.
  • the monitoring data is merged with the corresponding questionnaires to create a training set that serves as input to the QoE prediction models.
  • a new record is created for each subjective data and each network data containing said register the most significant parameters that can contribute to the QoE.
  • the method creates a training set where the result of the mixture of the network data with the questionnaires is stored.
  • Each of the training sets is used as input for the machine learning algorithms to obtain the prediction models. These prediction models predict the subjective response values of the questionnaires based on the input data.
  • prediction models can be applied depending on the scenario, such as decision trees, vector support machines, Bayesian networks, artificial neural networks, etc.
  • a final prediction model is defined, which combines all the predictions into a single QoE MOS value. These predictions are combined using a weighted voting scheme, where the votes for the weights are modeled according to machine learning regression models. Different regression models can be constructed for the final prediction model based on the data from the training set of the last questionnaire questions such as linear regression, SMO regression, etc.
  • the final QoE prediction model is implemented in the QoE prediction platform, which can also be part of any existing monitoring system. In this way, a value of MOS is calculated in real time for each new data of the data network In this stationary phase it is not necessary to use input data from the users.
  • the prediction platform may include other parameters such as confidence prediction or network parameters that can contribute the most to obtaining the QoE.
  • Figure 1. Shows a flow chart of the method to calculate the perception of user experience of the quality of the monitored services integrated in telecommunications operators, in a particular case.
  • the input data of the network is the IP detail records (IPDR) acquired from the network through passive probes, generating an IPDR record 'at the exit for each user when using a service from the videostraming protocols involved, such as RTP, RTSP, RTCP ...
  • IPDR IP detail records
  • IPDRs (1) The most important parameters that affect the customer experience are: content, user identifier, server identifier, packet loss, delay, jitter, performance, initiation time and error.
  • the detailed specifications and specific fields of these IPDRs (1) can be found in the recommendation "Quality of end-to-end monitoring service on converged IPTV platforms".
  • the key component in the correlation algorithms (4) is the definition of unique attributes in both data sets (network data and questionnaire data) that allows their proper correlation.
  • the correlation algorithm (4) is based on the following values:
  • the prediction model (5) used in this exemplary embodiment is constructed from the machine learning algorithm C4.5 together with AdaBoost (adaptive amplifier), an algorithm that creates a set of classifiers.
  • AdaBoost adaptive amplifier
  • AdaBoost is a meta-algorithm and can be used in combination with many other machine learning algorithms to improve its performance.
  • AdaBoost creates subsequent classifiers, emphasizing data that may have been previously misclassified.
  • the model combines all the classifiers together in a single set with weighted vote.
  • aggregation techniques based on the proximity of two nearby response values (for example, “excellent” and “very well, “are two of the possible responses with very similar subjective values)
  • a greater accuracy of the model is obtained.
  • the weights of the model are acquired by means of the vector support machine regression algorithm (SVM). In this way all the responses are combined with a regression model to give a single MOS QoE value (7).
  • SVM vector support machine regression algorithm
  • the preconfigured models for each issue about perceived quality are: ⁇
  • Each model is based on data from subjective questionnaires using the Weka 3.7 ML platform.
  • the prediction model is embedded in the QoE prediction platform, which is connected to the monitoring system.
  • IPDR ' The output of the QoE prediction platform is an expanded set of the incoming IPDRs, called as IPDR ', which adds the following attributes for each IPDR':
  • the application is intended to be highly configurable and adaptable to different functional configurations:

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Multimedia (AREA)
  • Business, Economics & Management (AREA)
  • General Business, Economics & Management (AREA)
  • Environmental & Geological Engineering (AREA)
  • Telephonic Communication Services (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)
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Abstract

La présente invention concerne un procédé pour calculer la perception d'expérience d'utilisateur de la qualité des services surveillés intégrés chez des opérateurs de télécommunications. A cet effet, le procédé fait appel à des données provenant de la surveillance des services utilisés par les utilisateurs conjointement à des questionnaires renseignés préalablement par un échantillon représentatif d'utilisateurs pour leur mélange ultérieur au moyen d'algorithmes de corrélation et après passage par des algorithmes d'apprentissage automatiques, obtenir?de ceux-ci une valeur de qualité de l'expérience que suppose une estimation de la qualité du service perçue par l'utilisateur qui utilise ledit service. Enfin, les paramètres de réseau qui affectent le plus la QoE sont automatiquement identifiés en fonction de leur importance sur la prédiction de la qualité afin de proposer les valeurs nécessaires pour atteindre une qualité d'expérience définie par l'utilisateur.
PCT/ES2010/070324 2010-05-14 2010-05-14 Procédé pour calculer la perception d'expérience d'utilisateur de la qualité des services surveillés intégrés chez des opérateurs de télécommunications WO2011141586A1 (fr)

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Application Number Priority Date Filing Date Title
PCT/ES2010/070324 WO2011141586A1 (fr) 2010-05-14 2010-05-14 Procédé pour calculer la perception d'expérience d'utilisateur de la qualité des services surveillés intégrés chez des opérateurs de télécommunications
EP10851321.9A EP2571195A4 (fr) 2010-05-14 2010-05-14 Procédé pour calculer la perception d'expérience d'utilisateur de la qualité des services surveillés intégrés chez des opérateurs de télécommunications
BR112012029162A BR112012029162A2 (pt) 2010-05-14 2010-05-14 método para calcular a percepção de experiência de usuário da qualidade dos serviços monitorados integrados em operadores de telecomunicações
US13/697,891 US20130148525A1 (en) 2010-05-14 2010-05-14 Method for calculating perception of the user experience of the quality of monitored integrated telecommunications operator services
ARP110101647A AR081041A1 (es) 2010-05-14 2011-05-12 Metodo para calcular la percepcion de experiencia de usuario de la calidad d elos servicios monitorizados integrados en operadores de telecomunicaciones

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PCT/ES2010/070324 WO2011141586A1 (fr) 2010-05-14 2010-05-14 Procédé pour calculer la perception d'expérience d'utilisateur de la qualité des services surveillés intégrés chez des opérateurs de télécommunications

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AR081041A1 (es) 2012-05-30
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US20130148525A1 (en) 2013-06-13
BR112012029162A2 (pt) 2017-02-21

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